Update app.py

app.py

@@ -15,7 +15,7 @@ import soundfile as sf
 
 # Load the model
 model = hub.load('Audio_Multiple_v1')
-
+solutions=[]
 def class_names_from_csv(class_map_csv_text):
     """Returns list of class names corresponding to score vector."""
     class_names = []
@@ -60,7 +60,7 @@ def process_audio_file(file_data, url):
     confidence_threshold = 0.60                 
     confident_classes = set()
 
-    solutions=[]
+
     exclusion_list = ['Mechanisms','Domestic animals, pets', 'Animal', 'Silence', 'Alarm', 'Wind chime', 'Water', 'Livestock, farm animals, working animals', 'Wild animals', 'Bleat', 'Siren', 'Computer keyboard', 'Toot', 'Shatter', 'Bird','Caw', 'Independent music', 'Tender music', 'Ocean', 'House music', 'Middle Eastern music', 'Swing music', 'Soul music', 'Shofar', 'Motor vehicle (road)', 'White noise','Pink noise', 'Cacophony', 'Sidetone', 'Static', 'Outside, rural or natural', 'Outside, urban or manmade', 'Inside, public space', 'Inside, large room or hall', 'Inside, small room', 'Sound effect'  ]
     for frame_scores in scores_np:
         for i, score in enumerate(frame_scores):